China COVID-19 Analysis

US Cases and Deaths by Date (Previous 30 Days)

• Data Source: European Centre for Disease Prevention and Control
• URL: https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide

# ggplotly(cases_1)

US %>% filter(Reported >= Sys.Date() - 60) %>%  ggplot() + geom_col(aes(x=Reported,y=log(Deaths),fill=Deaths)) +
  theme(axis.text.x = element_text(angle = 45)) +
      labs(x="Date Reported",y="Log Deaths",title="COVID-19: Deaths by Date",
           subtitle="(Logarithmic Scale)")

## Warning: Removed 10 rows containing missing values (geom_col).

US Accumulated Daily Total: Cases and Deaths

• Data Source: John Hopkins Coronavirus Resource Center
• URL: https://coronavirus.jhu.edu/map.html

US Recoveries Accoumliated and Daily

JHU_US %>% 
  ggplot() + geom_col(aes(x=Date,y=na.omit(Recovered),fill=Recovered)) +
  theme(axis.text.x = element_text(angle = 45)) +
      labs(title="China COVID-19: Accumulated  Recoveries by Date",x="Date Reported",y="Total Cases")  + scale_y_continuous(labels = scales::comma) +
  scale_fill_gradient(labels = scales::comma)

# WA <- JHU_US %>% group_by(Date) %>% 
# summarise(Cases=sum(Cases), Deaths = sum(Deaths))   %>%
#    mutate(daily_deaths = Deaths - lag(Deaths)) %>% 
#    mutate(daily_cases = Cases -  lag(Cases)) %>%
#   mutate(daily_recovered = Recovered -  lag(Recovered)) %>%
#    mutate(DeathRate = daily_deaths/daily_cases)

JHU_US %>% filter(Date >= Sys.Date() -60) %>%
  ggplot() + geom_col(aes(x=Date,y=na.omit(Recovered),fill=Recovered)) +
  theme(axis.text.x = element_text(angle = 45)) +
      labs(title="USA COVID-19: Accumulated  Recoveries by Date",x="Date Reported",y="Total Cases")  + scale_y_continuous(labels = scales::comma) +
  scale_fill_gradient(labels = scales::comma)